It global team lead

-Lead and manage the global IT team for data engineering that develops all technical artefacts as code, implemented in professional IDEs, with full version control and CI/CD automation. This team combines both lakehouse modeling of common and business use ase artefacts & semantics, as well as generalist data integration & metadata services. -Ensure high-quality delivery of data engineering assets that enable business analytics, AI/ML integration, and data governance at scale. -Act as the delivery and people manager for the data engineering team co-located in Bengaluru, collaborating globally with platform, business, and other IT stakeholders. -Drive consistency, engineering excellence, and cross-domain reuse across the entire data engineering lifecycle—from data acquisition to semantic layer delivery, while applying rigorous software engineering practices in data engineering such as modular design, test-driven development, and artifact reuse in all implementations -Direct management of approx. 10–15 data engineers (generalists and specialists). Reports to the global head of Data & Analytics within the IT Competence Center. -Team delivers data engineering & analytics assets via Product Owner for data & analytics to all business domains. -Collaborates with Product Owners, Lead Architects & Lead engineers, Data Governance, Infrastructure & Cybersecurity, and domain-aligned functional IT teams globally. Main Tasks - Line management for a high-performing, cross-functional data engineering team. - Drive skill development, mentorship, and performance management. - Foster a culture of accountability and trust. - Own timely delivery of data & analytics assets from data acquisition to semantic layers. - Align work with business priorities and architectural standards. - Ensure quality gates and documentation. - Act as primary escalation and coordination point across business domains. - Bridge infrastructure, functional IT, cybersecurity, and platform decisions. - Advocate for team in global forums. - Guide adoption of engineering best practices (TDD, CI/CD, Ia C) & guide building all technical artefacts as code, creating scalable batch and streaming pipelines in Azure Databricks using Py Spark and/or Scala -Leading the design and operation of scalable batch/stream pipelines in Databricks, including ingestion from structured/semi-structured sources and implementation of bronze/silver/gold layers under lakehouse governance. -Overseeing dimensional modeling and curated data marts for analytics use cases, while ensuring semantic layer compatibility and collaboration on enterprise 3 NF warehouse integration. -Ensuring high-quality engineering practices across data validation, CI/CD-enabled TDD, performance tuning, metadata governance, and stakeholder collaboration via agile methods. - Build an inclusive, high-performance team culture in Bengaluru. - Champion Dev Sec Ops, reuse, automation, and reliability. Commit all artifacts to version control with peer review and CI/CD integration - Ensure documentation, knowledge sharing, and continuous improvement. -Leading the design and operation of scalable, secure ingestion services—including CDC, delta, full-load, and SAP extractions via tools like Theobald Extract Universal. -Overseeing integration with APIs, legacy systems, Salesforce, and file-based sources, while aligning all interfaces with cybersecurity standards and compliance protocols. -Driving the development of the enterprise data catalog application, supporting dataset discoverability, metadata quality, and Unity Catalog–aligned access workflows. Qualifications Degree in Computer Science, Data Engineering, Information Systems, or related discipline. Certifications in software development and data engineering (e.g., Databricks DE Associate, Azure Data Engineer, or relevant Dev Ops certifications). Minimum 8 years in enterprise data engineering, including data ingestion and pipeline design. Experience across structured and semi-structured source systems is required. Demonstrated experience building production-grade codebases in IDEs, with test coverage and version control. Hands-on experience with secure SAP/API ingestion, lakehouse development in Databricks, and metadata-driven data platforms. Delivered high-impact enterprise data products in cross-functional environments. At least 3 years of team leadership or technical lead experience, including hiring, mentoring, and representing team interests in enterprise-wide planning forums. Demonstrated success leading globally distributed teams and collaborating with stakeholders across multiple time zones and cultures.